Operations | Monitoring | ITSM | DevOps | Cloud

Infrastructure and Operations (I&O) teams have long operated under a familiar paradox: the faster the business scales, the more pressure I&O absorbs. Every new application deployment, every endpoint added, and every cloud workload spun up generates more complexity, more risk and more tickets. The traditional responses to this pressure — more headcount, more tooling, more scripts, more APIs — have delivered incremental relief at best.

Modern applications are distributed, ephemeral and built from a dozen moving parts. To keep them reliable, you need real visibility: not just “is the server up?”, but“how is this request behaving, right now, across every component it touches?”. The good news is that the observability world has converged on a handful of open standards — Prometheus for metrics, OpenTelemetry for telemetry, plus battle-tested protocols like StatsD and NRPE.

Legacy infrastructure was not designed for the requirements of the AI era. While large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift creates a new frontier for enterprise AI, but deploying at the edge introduces significant manual complexity, interoperability issues, and security vulnerabilities.

The telco industry is undergoing a fundamental change. Over the past few years, the increasing maturity of cloud-native infrastructure has accelerated the movement from manually operated and hardware-centric systems to automated, software-defined platforms. Underpinning this change are open source initiatives such as the Sylva project. Sylva is hosted by Linux Foundation Europe and heavily backed by major telecom operators and vendors.

Performance problems in Rails applications are sneaky. Generally speaking, nobody opens tickets that say “my application is slower than it was last month (about 20%)”. What you do get instead are vague complaints from team members about a p95 latency that is climbing every week or a background job that used to take 2 seconds now taking 40 seconds to finish.

Satellite mission operators depend on telemetry to understand spacecraft health, ground system performance, and mission status in real-time. Operation signals help teams identify risks, investigate anomalies, and keep operations moving. When a spacecraft enters safe mode or signal strength drops during a contact window, teams need trusted telemetry immediately. But mission data moves quickly across operational systems, and every handoff makes it harder to control.

You can already split AI spend by team and by model. But that’s not what your CEO asks in the QBR. The question is what you got for it: what did it cost to ship that feature, to launch that campaign, to serve that customer. And is the AI bet behind it paying off? Now you can allocate AI spend to the outcomes you own: customer, product, feature, the strategic bet on the P&L. Not just the team that spent it.

A majority of office professionals (72%) believe they understand how to use AI for their job better than the team responsible for managing AI at their company. While it’s encouraging to see employees embrace AI with such confidence, organizations will want to ensure they are providing the tools, guidance, and safeguards needed to help employees use AI safely.

How do you pick one server monitoring tool when every vendor page promises the same thing? A few years ago, two monitoring vendor websites showed you two different products. Today you can open five and read nearly the same feature list on each one. Real-time dashboards, instant alerts, AI everywhere. That sameness has made evaluation harder than ever. The marketing tells you nothing, and the wrong choice follows your team for years, either as features nobody opens or as the one missed alert at 2 a.m.